DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Action is in response to the Amendment filed January 8, 2026. Claims 1 and 17-18 are amended. Claims 1-25 are pending and have been examined in this application.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63/155,409, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Application No. 63/155,409 at least does not disclose, wherein one or more of the plurality of obtained feature values are automatically determined at least in part using an automated feature extraction process that employs at least one processor-based trained machine learning model, wherein the at least one processor-based trained machine learning model is at least partially pretrained in a first stage using a first set of historical content items, and wherein a modification of at least one feature value determined by the at least one processor-based trained machine learning model, s used in a second stage to update at least one parameter of the at least one processor-based trained machine learning model.
Therefore, as the present application is a nonprovisional of the prior-filed application, Application No. 63/155,409; and the claims are not supported by the disclosure of the application, the current claims, 1-25 of present application do not receive priority to the filing date of Application No. 63/155,409.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
In claims 1 and 17-18 the limitation “obtaining a plurality of obtained feature values related to a content item, wherein each given one of the plurality of feature values corresponds to a respective one of a plurality of features, wherein one or more of the plurality of feature values are automatically determined at least in part using an automated feature extraction process that employs at least one processor-based trained machine learning model, wherein the at least one processor-based trained machine learning model is at least partially pretrained in a first stage using a first set of historical content items, and wherein a modification of at least one feature value determined by the at least one processor-based trained machine learning model, at least some of the feature values may be determined using an automated feature extraction process that employs at least one machine learning model and at least some of the automatically determined feature values may be modified using a manual process. For example, the at least one machine learning model may be updated based on at least some of the automatically determined feature values that are modified using the manual process” [0034]. This does not describe that any modification of a feature value determined by a processor-based pretrained machine learning model, is used in a second stage to update at least one parameter of the processor-based trained machine learning model. Accordingly, the limitation constitutes impermissible new matter. Claims 2-16 and 19-25 by being dependents of claims 1 and 18 respectively are also rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-25 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-25 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-25 are directed toward at least one judicial exception without significantly more. In accordance with MPEP 2106, the rationale for this determination is explained below:
Representative claim 1 is directed towards a method, claim 17 is directed towards an apparatus, and claim 18 is directed towards a non-transitory medium, which are statutory categories of invention.
Although, claim 1 is directed toward a statutory category of invention, the claim however, is directed toward a judicial exception namely an abstract idea. The limitations that set forth the abstract idea recite: applying the plurality of feature values to at least generates an influence score for each of the plurality of feature values, wherein the influence score for each of the plurality of feature values indicates an influence of each respective feature value on at least one performance indicator associated with the content item; generating, one or more recommendations for improving the at least one performance indicator associated with the content item using the influence score for each of the plurality of feature values, wherein at least one of the one or more recommendations for improving the at least one performance indicator associated with the content item comprises updating at least one of the plurality of feature values, having a first influence score, to a different feature value, having a different, improved influence score; and initiating at least one modification of the content item using at least one of the one or more recommendations to modify, the content item in accordance with the different feature value to improve an engagement of the content item. These limitations, describe commercial interactions including, advertising, marketing or sales activities or behavior; and business relations. Applicant specification states that “content item may be, for example, a text file, a video file or an image file, or combinations thereof, that represent advertisements or other marketing materials” [0032]. As such, the limitations are directed towards the abstract grouping of Certain Methods of Organizing Human Activity in prong one of step 2A of the Alice/Mayo test (see MPEP 2106.04(a)(2) II).
This judicial exception is not integrated into a practical application because, when analyzed as a whole under prong two of step 2A of the Alice/Mayo test (MPEP 2106.04(d)), the additional elements provided by the claim amount to insignificant extra-solution activity and merely using a computer as a tool to perform an abstract idea. In particular the claim recites the additional elements of, obtaining a plurality of feature values related to a content item, wherein each given one of the plurality of feature values corresponds to a respective one of a plurality of features, wherein one or more of the plurality of feature values are automatically determined using an automated feature extraction process that employs at least one processor-based trained machine learning model, wherein the at least one processor-based trained machine learning model is at least partially pretrained in a first stage using a first set of historical content items, and wherein a modification of at least one feature value determined by the at least one processor-based trained machine learning model, is used in a second stage to update at least one parameter of the at least one processor-based trained machine learning model, which amounts to obtaining a particular data source or type of data to be manipulated and data gathering necessary to implement the judicial exception. See MPEP 2106.05(g). While, the additional elements of one processor-based trained engagement prediction model that; wherein the at least one processor-based trained engagement prediction model is trained using a labeled training dataset and a supervised learning process; by at least one processor-based modification recommendation engine; automated; the method is performed by at least one processing device comprising a processor coupled to a memory; using at least one processing device, which are recited at a high level of generality, are the mere use of a computer as a tool to perform the abstract ideas. See MPEP 2106.05(f). Simply adding insignificant extra-solution activity and applying and or instructions to apply the abstract idea by computer components is not a practical application of the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claim does not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claim does not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claim does not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e)). Therefore, the claim does not, for example, purport to improve the functioning of a computer. Nor does it effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claim is directed to abstract ideas.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to insignificant extra-solution activity and applying the abstract idea via a computer. Viewing these limitations individually, the obtaining a plurality of feature values related to a content item, wherein each given one of the plurality of feature values corresponds to a respective one of a plurality of features, wherein one or more of the plurality of feature values are automatically determined using an automated feature extraction process that employs at least one processor-based trained machine learning model, wherein the at least one processor-based trained machine learning model is at least partially pretrained in a first training stage using a first set of historical content items, and wherein a modification of at least one feature value determined by the at least one processor-based trained machine learning model, in the first training stage, is used in a second training stage to update at least one parameter of the at least one processor-based trained machine learning model, are used for necessary data gathering and selecting particular data source to implement the abstract idea. The courts have recognized receiving, processing, and storing data; and receiving or transmitting data over a network to be well‐understood, routine, and conventional functions when they are claimed in a merely generic manner or as extra-solution activity. See MPEP 2106.05(d)II; Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362 (Fed. Cir. 2016); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015). Moreover, it is well understood, routine and conventional in the art to use pre-trained data as input to update a machine learning model/algorithm. See at least Yoo et al. (US 20230316737 A1); Huts et al. (US 20230067026 A1); Xue et al. (US 20210279500 A1); Zhou et al. (US 20230047628 A1); Green et al. (US 20240104896 A1); Bueche et al. (US 11978445 B1); Rivlin et al. (US 20150178321 A1).
Moreover, the additional limitations of a processor-based trained engagement prediction model, processor-based modification recommendation module, processor and memory, also, do not constitute significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment1. The steps associated with training a machine learning model recites the idea of a solution or outcome and fail to recite details of how the training improves a technology is to be accomplished. The processor-based trained machine learning model, processor-based trained engagement prediction model, supervised learning process, and modification recommendation engine are used as tools to perform the abstract idea as discussed above. Considered as an ordered combination, the additional elements of claim 1 add nothing that is not already present when the steps are considered separately. The additional elements of generic computer components and machine learning model (computing tool) are used to perform a marketing strategy. Merely applying an exception using general computer components cannot provide an inventive concept. See TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (mere recitation of concrete or tangible components is not an inventive concept). Therefore, the limitations of the claim as a whole, when viewed individually and as an ordered combination, do not amount to significantly more than the abstract idea.
An analysis of dependent claims 2-16, likewise, do not recite any limitations that would remedy the deficiencies outlined above. The claims only further add to the abstract idea, with no elements which integrate the abstract idea into a practical application or constitute significantly more. For instance; claims 2-5, directed towards analyzing data applied by artificial intelligence technique; claims 6-10, 12 directed to data manipulation through mathematical correlation; claims 14-15, directed to managing personal behavior and/or interaction between people; including following rules or instructions. Thus, while they may slightly narrow the abstract idea by further describing it, they do not make it less abstract and are rejected accordingly. Further still, claims 17-25 suffer from substantially the same deficiencies as outlined with respect to claims 1-16 and are also rejected accordingly.
Response to Arguments
Applicant's filed arguments have been fully considered but have been found persuasive in part.
A. Applicant’s priority objection is withdrawn based on the original limitation. However, upon further consideration priority is not given to the date of the provisional application in view of Applicant’s amendment of independent claims 1, 17 and 18.
B. Applicant's arguments regarding the 35 U.S.C. § 101 rejection that the independent claims limitations correspond to technical steps that cannot practically be performed in the human mind. The Examiner respectfully disagrees. Determining a feature value using historical content; updating a parameter/value; generating an influence score and recommendation based on the influence score, are all concepts capable of being performed in the human mind including using pen and paper. See MPEP 2106.04(a)(2) III. Notwithstanding the claims are directed to the abstract grouping of Certain Methods of Organizing Human Activity in prong one of step 2A of the Alice/Mayo because the claim limitations, describe commercial interactions including, advertising, marketing or sales activities or behavior; and business relations; as well as managing personal behavior (improving user engagement with the content item). Applicant specification states that “content item may be, for example, a text file, a video file or an image file, or combinations thereof, that represent advertisements or other marketing materials.” Published specification [0032]. Applicant’s specification, also makes it clear that although, it uses at least one machine learning model, “some of the automatically determined feature values are modified using a manual process.” Published specification [0006].
Applicant alludes to Example 39 of the USPTO's Subject Matter Eligibility Examples, which in this case is not apposite. Example 39 was found eligible because it was directed to a technique for training a neural network for facial detection, which was not an abstract idea. Training the neural network in two stages had no bearing on whether the claim was directed to an abstract idea. On the contrary, the instant claims do not reflect any such improvements. The instant Application is not for the purpose of training a neural network how to detect an image using non-abstract technology. “The one or more feature extraction models 520 may comprise custom feature extraction models and/or commercially available feature extraction models.” Published specification [0060].
Applicant contends that the claims improve a technical field and the functioning of computer devices by using processing feature value data to provide feature-level recommendations to improve the overall content. The Examiner respectfully disagrees. Processing data to provide recommendations to improve content is directed to an entrepreneurial improvement rather than a technological one; and using a two-phase training process as Applicant alludes to does not improve the computer or a technology, as it is merely a routine to process the data “to provide feature-level recommendations to improve the overall content.” Hence, such two-phased processing is a part of the abstract idea.
Applicant argues the independent claims recite additional elements that, when considered as a whole, integrate the abstract idea into a practical application by reciting a specific improvement to computer-related technology and the machine learning process itself. The Examiner respectfully disagrees. The claims are directed to recommendations for improving a performance indicator and user engagement, associated with advertising and/or marketing content. That this is applied by a machine learning model in two stages does not amount to an improvement to a technical field or the functioning of computer device. Applicant states that using modified feature values to update a parameter of the trained machine learning model improves the accuracy of the feature extraction and/or reduces the model generation time, and finds support in the Appeals Review Panel decision in Ex parte Desjardins. However, the Panel decision in Desjardins was that there was a technical improvement to the machine learning model found in the additional elements of the claim. This was technically supported by the specification, which described how this was accomplished. In contrast Applicant merely uses its model as a tool to generate predictions, scores and recommendation. There is no technical support/technical description in Applicant’s Specification as to how it improves the functioning of a machine learning model, the computing device itself, or any other technology/technical field. See MPEP 2106.05(a), “if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.”
Applicant states that updating of the processor-based trained machine learning model, as recited in the independent claims, will make them more accurate and/or quicker to generate, which comprises an improvement. The Examiner respectfully disagrees. The processor-based trained machine learning model, as shown, are directed to an entrepreneurial improvement rather than a technological one. Again, Applicant’s specification provides no technical evidence/technical support that the instant claimed invention, when implemented, improves the functioning of the computing device itself, or that it improves another technology or technical field.
Applicant further submits that the limitation “initiating at least one automated modification of the content item using at least one of the one or more recommendations to modify, using at least one processing device, the content item in accordance with the different feature value to improve an engagement of the content item” addresses difficulty of engaging with, and/or reacting favorably to digital content, and integrates the judicial exception into a practical application. The Examiner respectfully disagrees. Using a recommendation to modify content/advertisement to improve user engagement with said content/advertisement is an abstract idea in and of itself. Thus, the improvement provided by the claim is to the abstract idea. See Recentive Analytics, Inc. v. Fox Corp, 134 F.4th 1205, 1213? (Fed. Cir. 2025) (“requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement”)
Applicant states that the claims also recite a combination of features that amount to significantly more than the judicial exception. The Examiner respectfully disagrees. The claim limitations are directed to abstract ideas applied by computer components which do not go beyond applying the abstract idea by computer components, or generally linking the use of the abstract idea to a particular technological environment and as such does not provide an inventive concept that applies the abstract idea in some other meaningful way. Furthermore, because a claim discloses a specific solution to a particular problem does not automatically render it patent eligible. See Bilski v. Kappos, 561 U.S. 593, 599–601 (2010) (concluding that claims fell outside § 101 notwithstanding the fact that they disclosed a very specific method of hedging against price increases); Parker v. Flook, 437 U.S. 584, 593 (1978) (rejecting the argument “that if a process application implements a principle in some specific fashion, it automatically falls within the patentable subject matter of § 101”); and Alice v. CLS Bank, 134 S. Ct. 2347, 2358–60 (2014) (claims fell outside of 35 U.S.C. 101 even though they described a very specific method for conducting intermediated settlement). Therefore, the claims do not amount to significantly more than the judicial exception.
Based on the foregoing, the claims in view of Alice, the claims do not connote an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; and the claims do not move beyond a general link of the use of the abstract idea to a particular technological environment. Therefore, the 35 U.S.C. § 101 rejection is maintained.
C. Applicant’s arguments regarding the 35 U.S.C. § 112 rejection are moot in light of Applicant’s amendments. However, upon further consideration, new grounds of rejection are made in view of Applicant’s amendments of claims 1, 17 and 18.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Errol CARVALHO whose telephone number is (571)272-9987. The
Examiner can normally be reached on M-F 9:30-7:00 Alt Fri
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached on 571- 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E CARVALHO/
Primary Examiner, Art Unit 3622
1 See, Alice Corp. Pty Ltd. v. CLS Bank lnt'l, 134 S. Ct. 2347, 2360 (2014) (noting that none of the hardware recited “offers a meaningful limitation beyond generally linking ‘the use of the [method] to a particular technological environment,’ that is, implementation via computers” (citing Bilski v. Kappos, 561 U.S. 593, 610-11 (2010))).
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